The last step is scheduling which optimizes the execution of operators on a given device.
In TVM framework, a machine-learning based scheduling called “AutoTVM” [1]} is used to generate optimized codes.
For each operator in the optimized low-level graph, AutoTVM module uses Xgboost [2]} to search for the best schedule within the predefined search space. fig:fig2 (c) shows a generic multi-level loop nest implementation of the linear operator. The search space for such a linear operator is defined by one single knob, \(tile\_m: [1, m]\) , which determines the tiling parameter \(m\)  for input \(X\) .
